import contextlib
import inspect
import warnings

def _missing(package: str, model: str, error: Exception) -> ImportError:
    return ImportError(f"{model} needs the '{package}' package. Install with:\n  pip install {package}\n(original error: {error})")

def _patch_statsmodels_pandas_compat() -> None:
    """Bridge the legacy decorator call used by statsmodels 0.14.x."""
    try:
        import pandas.util._decorators as pandas_decorators
        pandas_original = pandas_decorators.deprecate_kwarg
        pandas_parameters = list(inspect.signature(pandas_original).parameters.values())
        if (
            pandas_parameters
            and pandas_parameters[0].name == 'klass'
            and not getattr(pandas_original, "_rinking_compat", False)
        ):
            def pandas_compatible_deprecate_kwarg(*args, **kwargs):
                if len(args) >= 2 and all(isinstance(value, str) for value in args[:2]):
                    return pandas_original(FutureWarning, args[0], args[1], *args[2:], **kwargs)
                return pandas_original(*args, **kwargs)

            pandas_compatible_deprecate_kwarg._rinking_compat = True
            pandas_decorators.deprecate_kwarg = pandas_compatible_deprecate_kwarg
    except ImportError:
        pass
    try:
        import statsmodels.compat.pandas as compat
    except ImportError:
        return
    signature = inspect.signature(compat.deprecate_kwarg)
    if len(signature.parameters) != 5:
        return
    original = compat.deprecate_kwarg
    if getattr(original, "_rinking_compat", False):
        return

    def compatible_deprecate_kwarg(*args, **kwargs):
        if len(args) >= 2 and all(isinstance(value, str) for value in args[:2]):
            return original(FutureWarning, args[0], args[1], *args[2:], **kwargs)
        return original(*args, **kwargs)

    compatible_deprecate_kwarg._rinking_compat = True
    compat.deprecate_kwarg = compatible_deprecate_kwarg

def import_arima(model_name: str):
    try:
        _patch_statsmodels_pandas_compat()
        from statsmodels.tsa.arima.model import ARIMA
    except ImportError as e:
        raise _missing('statsmodels', model_name, e) from e
    return ARIMA

def import_kpss(model_name: str):
    try:
        _patch_statsmodels_pandas_compat()
        from statsmodels.tsa.stattools import kpss
    except ImportError as e:
        raise _missing('statsmodels', model_name, e) from e
    return kpss

def import_arch_model(model_name: str):
    try:
        _patch_statsmodels_pandas_compat()
        from arch import arch_model
    except ImportError as e:
        raise _missing('arch', model_name, e) from e
    return arch_model

def fit_arima(model):
    """Fit statsmodels ARIMA with a bounded optimizer budget.

    Walk-forward evaluation can fit hundreds of models. An unbounded BFGS
    call makes a free Space look hung while adding little value to a short
    intraday window; the selected order is still re-estimated from each
    training window.
    """
    fit = model.fit(method_kwargs={'maxiter': 120, 'disp': False})
    if not getattr(fit, 'mle_retvals', {}).get('converged', False):
        raise ValueError('ARIMA optimizer did not converge within the bounded iteration budget.')
    if not __import__('numpy').isfinite(fit.params).all():
        raise ValueError('ARIMA fit produced non-finite parameters.')
    if not __import__('numpy').isfinite(fit.aic):
        raise ValueError('ARIMA fit produced non-finite AIC.')
    return fit

@contextlib.contextmanager
def quiet_fit():
    with warnings.catch_warnings():
        # Expected candidate-selection warnings only. Convergence is checked by
        # fit_arima; unrelated warnings are not blanket-suppressed.
        warnings.filterwarnings('ignore', message='Non-stationary starting autoregressive parameters.*')
        warnings.filterwarnings('ignore', message='Non-invertible starting MA parameters.*')
        warnings.filterwarnings('ignore', message='Maximum Likelihood optimization failed to converge.*')
        warnings.filterwarnings('ignore', message='The test statistic is outside of the range.*')
        yield